From 24a118fa098b3aca7adbf7ce5a8e5a25678f2630 Mon Sep 17 00:00:00 2001 From: Bruno Scaglione Date: Sun, 5 May 2024 16:03:13 -0300 Subject: [PATCH] changed word "algorithms" to "models" Although models are technically algorithms, in the field of ML the word "algorithm" generaally refers to the learning algorithm that spits the model as output. I think the using algorithms as a synonym for models can confuse the reader. --- contents/ml_systems/ml_systems.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contents/ml_systems/ml_systems.qmd b/contents/ml_systems/ml_systems.qmd index fa8cccae..6751d8ed 100644 --- a/contents/ml_systems/ml_systems.qmd +++ b/contents/ml_systems/ml_systems.qmd @@ -30,7 +30,7 @@ As we journey further into this chapter, we will demystify the intricate yet cap ## Machine Learning Systems -ML is rapidly evolving, with new paradigms emerging that are reshaping how these algorithms are developed, trained, and deployed. In particular, the area of embedded machine learning is experiencing significant innovation, driven by the proliferation of smart sensors, edge devices, and microcontrollers. This chapter explores the landscape of embedded machine learning, covering the key approaches of Cloud ML, Edge ML, and TinyML (@fig-cloud-edge-tinyml-comparison). +ML is rapidly evolving, with new paradigms emerging that are reshaping how models are developed, trained, and deployed. In particular, the area of embedded machine learning is experiencing significant innovation, driven by the proliferation of smart sensors, edge devices, and microcontrollers. This chapter explores the landscape of embedded machine learning, covering the key approaches of Cloud ML, Edge ML, and TinyML (@fig-cloud-edge-tinyml-comparison). ![Cloud vs. Edge vs. TinyML: The Spectrum of Distributed Intelligence](images/png/cloud-edge-tiny.png){#fig-cloud-edge-tinyml-comparison}